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      ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context

      1 , 2 , 2 , 3 , 2 , 4 , 5 , 3 , , 1 , 2

      BMC Bioinformatics

      BioMed Central

      NIPS workshop on New Problems and Methods in Computational Biology

      18122004

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          Abstract

          Background

          Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genome-wide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods.

          Results

          We prove that ARACNE reconstructs the network exactly (asymptotically) if the effect of loops in the network topology is negligible, and we show that the algorithm works well in practice, even in the presence of numerous loops and complex topologies. We assess ARACNE's ability to reconstruct transcriptional regulatory networks using both a realistic synthetic dataset and a microarray dataset from human B cells. On synthetic datasets ARACNE achieves very low error rates and outperforms established methods, such as Relevance Networks and Bayesian Networks. Application to the deconvolution of genetic networks in human B cells demonstrates ARACNE's ability to infer validated transcriptional targets of the cMYC proto-oncogene. We also study the effects of misestimation of mutual information on network reconstruction, and show that algorithms based on mutual information ranking are more resilient to estimation errors.

          Conclusion

          ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.

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          Most cited references 36

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          Emergence of scaling in random networks

          Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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            The structure and function of complex networks

             M. Newman (2003)
            Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
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              Cluster analysis and display of genome-wide expression patterns

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                Author and article information

                Conference
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central (London )
                1471-2105
                2006
                20 March 2006
                : 7
                : Suppl 1
                : S7
                Affiliations
                [1 ]Department of Biomedical Informatics, Columbia University, New York, NY 10032
                [2 ]Joint Centers for Systems Biology, Columbia University, New York, NY 10032
                [3 ]Institute for Cancer Genetics, Columbia University, New York, NY 10032
                [4 ]Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10032
                [5 ]IBM T.J. Watson Research Center, Yorktown Heights, NY 10598
                Article
                1471-2105-7-S1-S7
                10.1186/1471-2105-7-S1-S7
                1810318
                16723010
                NIPS workshop on New Problems and Methods in Computational Biology
                Whistler, Canada
                18122004
                Categories
                Proceedings

                Bioinformatics & Computational biology

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